Field of the Invention
The invention relates in general to a communication system, and more particularly to a soft output decoder for a receiver of convolutional coding communication.
Description of the Related Art
In the process of data transmission of a digital communication system, incorrect messages may be received at a receiver end frequently due to unpredictable interference. Without increasing the transmission power, channel coding, although effectively reduces the error rate, poses a setback of occupying the transmission bandwidth. In view of the increasing demand of data transmission and storage systems of the public, not only the transmission rate will get faster but also the quality of service (QoS) will get higher in the future. As channel coding ensures that an error of the transmission bit is controlled within a certain range, channel coding is a critical consideration in the system design.
Convolutional coding is often used in channel coding to prevent a receiver from receiving incorrect messages. At a transmitting end, a code vector or an information block transmitted may be described by a trellis diagram. The complexity of a trellis diagram is determined by a constraint length of an encoder. Although the operation complexity gets higher as the length of the constraint length gets longer, such coding relatively provides better robustness.
At a receiving end, a soft-decision coder may be adopted to identify a maximum likelihood code vector through a Viterbi algorithm and trellis architecture to perform decoding. However, the operation complexity of a the Viterbi algorithm exponentially increases as the constraint length gets longer. In other words, compared to convolutional coding having a longer constraint length, a Viterbi decoder may require a substantial amount of memory and consume significant power to process the operation.
Turbo coding is proven to render better performance than common coding technologies. A turbo code is formed from processing two or more convolutional codes by a turbo interleaver. To decode turbo codes, convolutional codes are individually decoded by a soft-decision decoder using an iteration approach. A soft-decision decoder decodes a convolutional code to provide extrinsic information, which allows the soft-decision decoder to provide a more accurate result when the soft-decision decoder decodes another convolutional code. In the prior art, soft-decision decoding may adopt a maximum a posterior (MAP) algorithm or a soft output Viterbi algorithm (SOVA), both of which requiring forward recursion and backward recursion for decoding to determine the soft output of one information block. In general, in an environmental with a lower signal-to-noise ratio (SNR), turbo codes render better performance than other convolutional codes.
One type of decoder that directly implements the MAP algorithm performs forward recursion on a trellis of one entire information block, and then performs backward recursion. However, such decoding method not only requires a large amount of memory space but also leads to a severe communication latency, and is not a practically feasible approach.
In the prior art, window technologies are introduced to reduce the required memory space by means of an additional operation amount. That is, the operation amount is trade-off with the memory space. In simple, in window technologies, a code block is divided into a plurality of sub-trellises using a window of a certain size, and only one sub-trellis is decoded each time. As only one sub-trellis is computed, the demand on memory space is smaller. However, window technologies need additional training operations in order to allow the state metric of two boundaries of each sub-trellis to be sufficiently representative.